--- title: "gpt-researcher vs deep-research" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/assafelovic-gpt-researcher-vs-dzhng-deep-research" tools: ["assafelovic-gpt-researcher", "dzhng-deep-research"] --- # gpt-researcher vs deep-research Both gpt-researcher (assafelovic-gpt-researcher) and deep-research (dzhng-deep-research) are open-source AI tools aimed at facilitating deep research tasks using large language models, web scraping, and search engine integration. | | [gpt-researcher](/tools/assafelovic-gpt-researcher.md) | [deep-research](/tools/dzhng-deep-research.md) | | --- | --- | --- | | Tagline | An autonomous agent that conducts deep research on any data using any LLM providers | An AI-powered research assistant that performs iterative, deep research on any topic | | Stars | 28,146 | 19,312 | | Forks | 3,803 | 1,973 | | Open issues | 210 | 90 | | Language | Python | TypeScript | | Adopt for | GPT Researcher is an open-source deep research agent that conducts thorough and unbiased web or local document analysis, producing comprehensive reports with inline images and detailed citations. It uses a 'planner' and | Deep Research is an AI-powered research assistant that efficiently dives into any topic by leveraging search engines, web scraping, and large language models. It iteratively refines its research direction over time to go | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents | Data & Retrieval, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gpt-researcher](/tools/assafelovic-gpt-researcher.md) | [deep-research](/tools/dzhng-deep-research.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 2d | 87d | | Open issues (now) | 210 | 90 | | Security scan | 62 low (62 low) | 39 low (39 low) | | Full report | [trust report](/tools/assafelovic-gpt-researcher/trust.md) | [trust report](/tools/dzhng-deep-research/trust.md) | **Typed relationship:** gpt-researcher _(alternative)_ deep-research Both tools aim to conduct deep research using AI, with the primary difference being their implementation approaches. ## Shared compatibility - **Node.js**: [gpt-researcher](/tools/assafelovic-gpt-researcher.md) - Node.js runtime; [deep-research](/tools/dzhng-deep-research.md) - Node.js runtime ## Decision facts: gpt-researcher - **Requirements:** Min 4 GB RAM; - A Python environment needs to be set up.; - Google Gemini (Nano Banana) integration for AI-generated images requires specific setup and keys. - **Adopt for:** GPT Researcher is an open-source deep research agent that conducts thorough and unbiased web or local document analysis, producing comprehensive reports with inline images and detailed citations. It uses a 'planner' and - **License detail:** Apache-2.0 ## Decision facts: deep-research - **Requirements:** Min 2 GB RAM; Requires Docker; Requires Node.js environment setup.; Needs specific API keys for third-party web search (Firecrawl) and language model services (OpenAI). - **Adopt for:** Deep Research is an AI-powered research assistant that efficiently dives into any topic by leveraging search engines, web scraping, and large language models. It iteratively refines its research direction over time to go ## Choose when ### Choose gpt-researcher if… - gpt-researcher is primarily Python; deep-research is TypeScript. - License: gpt-researcher is Apache-2.0, deep-research is MIT. - Requirements: Min 4 GB RAM; - A Python environment needs to be set up.; - Google Gemini (Nano Banana) integration for AI-generated images requires specific setup and keys.. - Both tools aim to conduct deep research using AI, with the primary difference being their implementation approaches. - Tags unique to gpt-researcher: llms, deepresearch, python, automation. - - You need to generate objective and detailed research reports beyond 2,000 words using both web sources and local documents. ### Choose deep-research if… - deep-research is primarily TypeScript; gpt-researcher is Python. - License: deep-research is MIT, gpt-researcher is Apache-2.0. - Requirements: Min 2 GB RAM; Requires Docker; Requires Node.js environment setup.; Needs specific API keys for third-party web search (Firecrawl) and language model services (OpenAI).. - Both tools aim to conduct deep research using AI, with the primary difference being their implementation approaches. - Tags unique to deep-research: research, o3-mini, gpt. - Also covers Data & Retrieval. - You require a detailed and comprehensive report on a specific topic where traditional manual or less refined automated tools are too basic. ## When NOT to use gpt-researcher - - Your project requires real-time or interactive research with immediate feedback, as GPT Researcher focuses on in-depth analysis rather than quick responses. - - You are working within a restricted network environment where web scraping is not permitted, since the tool relies heavily on online sources for data gathering. ## When NOT to use deep-research - If your project requires proprietary or classified data analysis because Deep Research relies on public web scraping and search engines, which limits access to non-public content. - You are looking for a tool that operates solely offline; since the tool needs internet access to perform its tasks through API calls to services like Firecrawl and OpenAI. ## Common questions ### How do gpt-researcher and deep-research compare in terms of customization? gpt-researcher provides more explicit customization options, allowing users to create domain-specific agents tailored to their unique research needs. In contrast, deep-research offers iterative refinement based on public web resources, which can adapt over time but doesn't emphasize customizable settings for specific domains. ### Which tool would be a better fit if one requires offline research? Neither gpt-researcher nor deep-research is optimally designed for offline research as both rely heavily on accessing the internet to perform tasks through web scraping and interaction with online APIs. Thus, neither would suit an environment devoid of internet access. ### How does each tool handle visual content in their reports? gpt-researcher stands out for its capability to include AI-generated visuals such as images into the research report, powered by integration with services like Google Gemini (Nano Banana). deep-research, on the other hand, places its emphasis more directly on text-based data retrieval and processing without highlighted features for integrating visual content. ### What is the difference between gpt-researcher and deep-research? gpt-researcher: An autonomous agent that conducts deep research on any data using any LLM providers. deep-research: An AI-powered research assistant that performs iterative, deep research on any topic. See the comparison table for live GitHub stats and shared categories. ### When should I choose gpt-researcher over deep-research? Choose gpt-researcher over deep-research when gpt-researcher is primarily Python; deep-research is TypeScript; License: gpt-researcher is Apache-2.0, deep-research is MIT; Requirements: Min 4 GB RAM; - A Python environment needs to be set up.; - Google Gemini (Nano Banana) integration for AI-generated images requires specific setup and keys.; Both tools aim to conduct deep research using AI, with the primary difference being their implementation approaches; Tags unique to gpt-researcher: llms, deepresearch, python, automation; - You need to generate objective and detailed research reports beyond 2,000 words using both web sources and local documents. ### When should I choose deep-research over gpt-researcher? Choose deep-research over gpt-researcher when deep-research is primarily TypeScript; gpt-researcher is Python; License: deep-research is MIT, gpt-researcher is Apache-2.0; Requirements: Min 2 GB RAM; Requires Docker; Requires Node.js environment setup.; Needs specific API keys for third-party web search (Firecrawl) and language model services (OpenAI).; Both tools aim to conduct deep research using AI, with the primary difference being their implementation approaches; Tags unique to deep-research: research, o3-mini, gpt; Also covers Data & Retrieval; You require a detailed and comprehensive report on a specific topic where traditional manual or less refined automated tools are too basic. ### When should I avoid gpt-researcher? - Your project requires real-time or interactive research with immediate feedback, as GPT Researcher focuses on in-depth analysis rather than quick responses. - You are working within a restricted network environment where web scraping is not permitted, since the tool relies heavily on online sources for data gathering. ### When should I avoid deep-research? If your project requires proprietary or classified data analysis because Deep Research relies on public web scraping and search engines, which limits access to non-public content. You are looking for a tool that operates solely offline; since the tool needs internet access to perform its tasks through API calls to services like Firecrawl and OpenAI. ### Is gpt-researcher or deep-research more popular on GitHub? gpt-researcher has more GitHub stars (28,146 vs 19,312). Stars measure visibility, not whether either tool fits your constraints. ### Are gpt-researcher and deep-research open source? Yes - both are open-source projects on GitHub (gpt-researcher: Apache-2.0, deep-research: MIT). ### Where can I find alternatives to gpt-researcher or deep-research? GraphCanon lists graph-backed alternatives at /tools/assafelovic-gpt-researcher/alternatives and /tools/dzhng-deep-research/alternatives (/tools/assafelovic-gpt-researcher/alternatives.md, /tools/dzhng-deep-research/alternatives.md), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at /compare/assafelovic-gpt-researcher-vs-dzhng-deep-research.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gpt-researcher or deep-research? gpt-researcher: Very active. deep-research: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for gpt-researcher and deep-research? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: gpt-researcher: /tools/assafelovic-gpt-researcher/trust; deep-research: /tools/dzhng-deep-research/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=assafelovic-gpt-researcher`](/api/graphcanon/graph?tool=assafelovic-gpt-researcher) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_